This study analyzes the relative accuracy of experts, polls, and the so-called ‘fundamentals’ in predicting the popular vote in the four U.S. presidential elections from 2004 to 2016. Although the majority (62%) of 452 expert forecasts correctly predicted the directional error of polls, the typical expert’s vote share forecast was 7% (of the error) less accurate than a simple polling average from the same day. The results further suggest that experts follow the polls and do not sufficiently harness information incorporated in the fundamentals. Combining expert forecasts and polls with a fundamentals-based reference class forecast reduced the error of experts and polls by 24% and 19%, respectively. The findings demonstrate the benefits of combining forecasts and the effectiveness of taking the outside view for debiasing expert judgment.

In this article a broad perspective incorporating elements of time series theory is presented for conceptualizing the data obtained in multi-trial judgment experiments. Recent evidence suggests that sequential context effects, assimilation and contrast, commonly found in psychophysical judgment tasks, may be present in judgments of abstract magnitudes. A time series approach for analyzing single-subject data is developed and applied to expert prognostic judgments of risk for heart disease with an emphasis on detecting possible sequential context effects. The results demonstrate that sequential context effects do exist in such expert prognostic judgments. Contrast and assimilation were produced by cue series; the latter occurring more frequently. Experts also showed assimilation of prior responses that was independent of the cue series input. Time series analysis also revealed that abrupt or large trial-by-trial changes in the value of cues that receive the most attention in prognostic judgment tasks can disrupt the accuracy of these judgments. These findings suggest that a time series approach is a useful alternative to ordinary least squares regression, providing additional insights into the cognitive processes operating during multi-cue judgment experiments.

As Antarctic subglacial lake research progresses to in situ exploration an important topic is the lake's probable gas concentration. Depending on hydrological setting, subglacial lakes may contain large amounts of dissolved gas or gas trapped within clathrates. Consequently, access can be potentially dangerous due to the risk of blowout where depressurization could lead to high-speed ejection of water and gas from a borehole. We present a structured approach to assess the blowout risk in subglacial lake exploration. The approach integrates a generic event tree, applicable to open and closed hydrological systems, with site-specific expert judgment incorporating rigorous probabilistic formulations. The methodology is applied to a motivating example: Ellsworth Subglacial Lake. Judgments elicited through a formal process were provided by five experts with 88 years combined experience that, after aggregation, gave a median risk of blowout of 1 in 2186 with a lower quartile of 1 in 3433 and an upper quartile of 1 in 1341. This approach can be applied to any subglacial lake given a modicum of knowledge on its hydrological setting, as uncertainty can be captured through the elicited judgments. Additionally, the event tree analysis informs blowout mitigation strategies to reduce risk of injury or death.

A prudent assessment of dependence is crucial in many stochastic models for insurance risks. Copulas have become popular to model such dependencies. However, estimation procedures for copulas often lead to large parameter uncertainty when observations are scarce. In this paper, we propose a Bayesian method which combines prior information (e.g. from regulators), observations and expert opinion in order to estimate copula parameters and determine the estimation uncertainty. The combination of different sources of information can significantly reduce the parameter uncertainty compared to the use of only one source. The model can also account for uncertainty in the marginal distributions. Furthermore, we describe the methodology for obtaining expert opinion and explain involved psychological effects and popular fallacies. We exemplify the approach in a case study.